Multiple Linear Regression Analysis- Summary final.pptx

# Multiple Linear Regression Analysis- Summary final.pptx -...

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Multiple Linear Regression Analysis- Summary

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Data Types Numerical data – Arithmetic can be performed on it. Categorical data – Can be nominal or ordinal Nominal data – magnitude does not matter but represents something. Religion, political party etc. Ordinal data – there is a natural ordering based on values
Purpose of Linear Regression Analysis To fit linear line (plane) to data for predicting To examine how different variables influence a variable The objective of multiple linear regression analysis is to find the relationship between a dependent variable and several independent (predictor) variables. Linear Regression Analysis estimates the coefficients of the linear equation, involving one or more independent variables, that best predict the value of the dependent variable. For example, you can try to predict a price of a house (the dependent variable) from independent variables such as square foot area, number of rooms, type of house and so on.

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Inputs and assumptions Data. The dependent and independent variables should be quantitative. Nominal variables such as gender, region, political party should be recoded as binary (dummy) variables or other types of contrast variables. Assumptions: 1. The distribution of the dependent variable must be normal for each value of the independent variable. 2. The variance of the distribution of the dependent variable should be constant for all values of the independent variable. 3. The relationship between the dependent variable and each independent variable should be linear, and 4. All observations should be independent.
In other words: There is a population regression line. It joins the means of the dependent variable for all values of the explanatory variables.

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